AdaBoost algorithm Consider the following labeled data (x, y) E R^2 (i is the example index): In this problem, you will use AdaBoost to learn a hidden function from this set of training examples. We will use two rounds of AdaBoost to learn a hypothesis for this data set. In round number t, AdaBoost chooses a weak learner that minimizes the weighted error Et. As weak learners, you will use axis parallel lines as I demonstrated in the class. Using the Ada Boost algorithm that I covered in the class, please answer the following questions: (1) The rst step of AdaBoost is to create an initial data weight distribution D1. What are the initial weights given to data points with index 4 and 7 by the AdaBoost algorithm, respectively? (2) Which is the hypothesis h1 that minimizes the weighted error in the rst round of AdaBoost, using the distribution D1 computed in the above question? (3) What is the weighted error of h1 computed above? (4) After computing h1 in the previous questions, we proceed to round 2 of AdaBoost. We begin by recomputing data weights depending on the error of h1 and whether a point was (mis)classied by h1. What are the weights given to data points with index 4 and 7 according to the distribution after round 1, D2, respectively? Do not forget to normalize the new data weights so that they sum to 1. (5) Which is the hypothesis h2 that minimizes the weighted error in the second round of AdaBoost, using the distribution D2 computed in the above question? (6) What is the weight assigned to the hypothesis of round 2, h2? (7) Now that we have completed two rounds of AdaBoost, it is time to create the final output hypothesis. What is the final weighted hypothesis after two rounds of AdaBoost? (8) Submit your source code with sucient comments. (9) Complete two iterations of bagging algorithm using the similar weak learner as the AdaBoosting and give your comments

Note: Do not call functions while using python

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